Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods, Acta Pharmaceutica Sinica B, https://doi. Graphical abstractTwenty structures including 19 SARS-CoV-2 targets and 1 human target were built by homology modeling. Library of ZINC drug database, natural products, 78 anti-viral drugs were screened against these targets plus human ACE2. This study provides drug repositioning candidates and targets for further in vitro and in vivo studies of SARS-CoV-2. (Mengzhu Zheng), xingzhouli@aliyun.com (Xingzhou Li). † These authors made equal contributions to this work.Abstract SARS-CoV-2 has caused tens of thousands of infections and more than one thousand deaths. There are currently no registered therapies for treating coronavirus infections. Because of time consuming process of new drug development, drug repositioning may be the only solution to the epidemic of sudden infectious diseases. We systematically analyzed all the proteins encoded by SARS-CoV-2 genes, compared them with proteins from other coronaviruses, predicted their structures, and built 19 structures that could be done by homology modeling. By performing target-based virtual ligand screening, a total of 21 targets (including two human targets) were screened against compound libraries including ZINC drug database and our own database of natural products. Structure and screening results of important targets such as 3-chymotrypsin-like protease (3CLpro), Spike, RNA-dependent RNA polymerase (RdRp), and papain like protease (PLpro) were discussed in detail. In addition, a database of 78 commonly used anti-viral drugs including those currently on the market and undergoing clinical trials for SARS-CoV-2 was constructed. Possible targets of these compounds and potential drugs acting on a certain target were predicted. This study will provide new lead compounds and targets for further in vitro and in vivo studies of SARS-CoV-2, new insights for those drugs currently ongoing clinical studies, and also possible new strategies for drug repositioning to treat SARS-CoV-2 infections.

Despite clear epidemiological and genetic evidence for X-linked prostate cancer risk, all prostate cancer genes identified are autosomal. Here we report somatic inactivating mutations and deletion of the X-linked FOXP3 gene residing at Xp11.23 in human prostate cancer. Lineage-specific ablation of FoxP3 in the mouse prostate epithelial cells leads to prostate hyperplasia and prostate intraepithelial neoplasia. In both normal and malignant prostate tissues, FOXP3 is both necessary and sufficient to transcriptionally repress cMYC, the most commonly over-expressed oncogene in prostate cancer as well as among the aggregates of other cancers. FOXP3 is an X-linked prostate tumor suppressor in the male. Since the male has only one X chromosome, our data represents a paradigm of “single-genetic-hit” inactivation-mediated carcinogenesis.

Background:The characteristics, significance and potential cause of positive SARS-CoV-2 diagnoses in recovered coronavirus disease 2019 (COVID-19) patients post discharge (re-detectable positive, RP) remained elusive.Methods: A total of 262 COVID-19 patients discharged from January 23 to February 25, 2020 were enrolled into this study. RP and non-RP (NRP) patients were grouped according to disease severity, and the characterization at re-admission was analyzed. SARS-CoV-2 RNA and plasma antibody levels were measured, and all patients were followed up for at least 14 days, with a cutoff date of March 10, 2020.Results: A total of 14.5% of RP patients were detected. These patients were characterized as young and displayed mild and moderate conditions compared to NRP patients while no severe patients were RP. RP patients displayed fewer symptoms but similar plasma antibody levels during their hospitalization compared to NRP patients. Upon hospital readmission, these patients showed no obvious symptoms or disease progression. All 21 close contacts of RP patients were tested negative for viral RNA and showed no suspicious symptoms. Eighteen out of 24 of RNA-negative samples detected by the commercial kit were tested positive for viral RNA using a hyper-sensitive method, suggesting that these patients were potential carriers of the virus after recovery from COVID-19.Conclusions: Our results indicated that young patients, with a mild diagnosis of COVID-19 are more likely to display RP status after discharge. These patients show no obvious symptoms or disease progression upon re-admission. More sensitive RNA detection methods are required to monitor these patients. Our findings provide information and evidence for the management of convalescent COVID-19 patients.

Autophagy, a highly conserved cellular proteolysis process, has been involved in non-small cell lung cancer (NSCLC). We tried to develop a prognostic prediction model for NSCLC patients based on the expression profiles of autophagy-associated genes. Univariate Cox regression analysis was used to determine autophagy-associated genes significantly correlated with overall survival (OS) of the TCGA lung cancer cohort. LASSO regression was performed to build multiple-gene prognostic signatures. We found that the 22-gene and 11-gene signatures could dichotomize patients with significantly different OS and independently predict the OS in TCGA lung adenocarcinoma (HR=2.801, 95% CI=2.252-3.486, P<0.001) and squamous cell carcinoma (HR=1.105, 95% CI=1.067-1.145, P<0.001), respectively. The prognostic performance of the 22-gene signature was validated in four GEO lung cancer cohorts. Moreover, GO, KEGG, and GSEA analyses unveiled several fundamental signaling pathways and cellular processes associated with the 22-gene signature in lung adenocarcinoma. We also constructed a clinical nomogram with a concordance index of 0.71 to predict the survival possibility of NSCLC patients by integrating clinical characteristics and the autophagy gene signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. Overall, we constructed and verified a novel autophagy-associated gene signature that could improve the individualized outcome prediction in NSCLC.

Objectives To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. Methods A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. Results Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm 3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance. Conclusions The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. Key Points • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.